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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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改进了对机器学习相关性准确性的估计 脑-表型关联 脑-表型关联

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神经科学中的机器学习可以预测大脑现象类型的关联,但估计最大可实现的预测准确性 (MAPA) 是一个挑战. 一个新的双重机器学习估计器改善了神经成像数据的MAPA估计,仅使用成像就揭示了心理病理学的有限预测能力.

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 生物统计学 生物统计学

背景情况:

  • 机器学习 (ML) 对于分析大脑表型关联和从神经成像数据进行个体预测至关重要.
  • 目前使用皮尔森相关的方法来估计模型准确性,特别是最大可实现的预测准确性 (MAPA),是不可靠的,可能需要数百万个样本.

研究的目的:

  • 为了正式定义MAPA,并证明皮尔森估计器对这个数量的偏差.
  • 开发和验证MAPA的新型半参数 (双机器学习) 估计器,提供准确的估计和有效的置信区间.

主要方法:

  • 最大可实现的预测准确度 (MAPA) 的正式定义.
  • 开发一个半参数 (双机器学习) 一步估计器.
  • 使用可再生大脑图表数据集的验证,分析神经成像数据与年龄和精神病理表型.

主要成果:

  • 皮尔森的相关性估计器对MAPA有偏见,其置信区间不足.
  • 拟议的双重机器学习估计器显示,从神经成像数据中估计大脑表型关联的偏差减少.
  • 使用神经成像进行心理病理因子评分的MAPA并不优于单独使用人口和麻烦共变量.

结论:

  • 开发的双重机器学习估计器为评估神经成像研究中的MAPA提供了更可靠的方法.
  • 与基本共变量相比,神经成像数据本身可能对心理病理因素得分的预测能力有限.
  • 对MAPA的准确估计对于在神经科学研究中推进ML应用至关重要.